class_docstr: Render label insights performed by :py:class:`~autogluon.eda.analysis.dataset.LabelInsightsAnalysis`.
  
  The following insights can be rendered:
  
  - classification: low cardinality classes detection
  - classification: classes present in test data, but not in the train data
  - regression: out-of-domain labels detection
  
  Examples
  --------
  >>> import autogluon.eda.analysis as eda
  >>> import autogluon.eda.visualization as viz
  >>> import autogluon.eda.auto as auto
  >>> auto.analyze(
  >>> auto.analyze(train_data=..., test_data=..., label=..., anlz_facets=[
  >>>     eda.dataset.ProblemTypeControl(),
  >>>     eda.dataset.LabelInsightsAnalysis(low_cardinality_classes_threshold=50, regression_ood_threshold=0.01),
  >>> ], viz_facets=[
  >>>     viz.dataset.LabelInsightsVisualization()
  >>> ])
  
  Parameters
  ----------
  headers: bool, default = False
      if `True` then render headers
  namespace: str, default = None
      namespace to use; can be nested like `ns_a.ns_b.ns_c`
  
  See Also
  --------
  :py:class:`~autogluon.eda.analysis.dataset.ProblemTypeControl`
  :py:class:`~autogluon.eda.analysis.dataset.LabelInsightsAnalysis`
code: def __init__(
      self,
      headers: bool = False,
      namespace: Optional[str] = None,
      **kwargs,
  ) -> None:
      super().__init__(namespace, **kwargs)
      self.headers = headers
  
imports: from typing import Any, Dict, List, Optional
  import pandas
  from pandas import DataFrame
  from ..state import AnalysisState
  from .base import AbstractVisualization
  from .jupyter import JupyterMixin
label_desc: Use image node_id 1 to create a new LabelInsightsVisualization object from inherited base classes: AbstractVisualization, JupyterMixin with example: obj = LabelInsightsVisualization(headers, namespace)